Automation, Protection and Control of Substation Based on IEC 61850

Reliability of power system protection system has been a key issue in the substation operation due to the use of multi-vendor equipment of proprietary features, environmental issues, and complex fault diagnosis. Failure to address these issues could have a significant effect on the performance of the entire electricity grid. With the introduction of IEC 61850 standard, substation automation system (SAS) has significantly altered the scenario in utilities and industries as indicated in this thesis

Research and technology: 1986 annual report of the Lyndon B. Johnson Space Center

Johnson Space Center accomplishments in new and advanced concepts during 1986 are highlighted. Included are research funded by the Office of Aeronautics and Space Technology; Solar System Exploration and Life Sciences research funded by the Office of Space Sciences and Applications; and Advanced Programs tasks funded by the Office of Space Flight. Summary sections describing the role of the Johnson Space Center in each program are followed by one-page descriptions of significant projects. Descriptions are suitable for external consumption, free of technical jargon, and illustrated to increase ease of comprehension

K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

The quality of the hatching process influences the success of the hatch rate besides the inherent egg factors. Eliminating infertile or dead eggs and monitoring embryonic growth are very important factors in efficient hatchery practices. This process aims to sort eggs that only have embryos to remain in the incubator until the end of the hatching process. This process aims to sort eggs with embryos to remain hatched until the end. Maximum checking is done the first week in the hatching period. This study aims to detect the presence of embryos in eggs. Detection of the existence of embryos is processed using segmentation. Egg images are segmented using the K-means algorithm based on Lab color images. The results of the image acquisition are converted into Lab color space images. The results of Lab color space images are processed using K-means for each color. The K-means process uses cluster k=3, where this cluster divides the image into three parts: background, eggs, and yolk. Egg yolks are part of eggs that have embryonic characteristics. This study applies the concept of color in the initial segmentation and grayscale in the final stages. The initial phase results show that the image segmentation results using k-means clustering based on Lab color space provide a grouping of three parts. At the grayscale image processing stage, the results of color image segmentation are processed with grayscaling, image enhancement, and morphology. Thus, it seems clear that the yolk segmented shows the presence of egg embryos. Based on this process and results, the initial stages of the embryo detection process used K-means segmentation based on Lab color space. The evaluation uses MSE and MSSIM, with values of 0.0486 and 0.9979; this can be used as a reference that the results obtained can detect embryos in egg yolk. This protocol could be used in a non-destructive quantitative study on embryos and their morphology in a precision poultry production system in the future

Investigation of a holistic human-computer interaction (HCI) framework to support the design of extended reality (XR) based training simulators

In recent years, the use of Extended Reality (XR) based simulators for training has increased rapidly. In this context, there is a need to explore novel HCI-based approaches to design more effective 3D training environments. A major impediment in this research area is the lack of an HCI-based framework that is holistic and serves as a foundation to integrate the design and assessment of HCI-based attributes such as affordance, cognitive load, and user-friendliness. This research addresses this need by investigating the creation of a holistic framework along with a process for designing, building, and assessing training simulators using such a framework as a foundation. The core elements of the proposed framework include the adoption of participatory design principles, the creation of information-intensive process models of target processes (relevant to the training activities), and design attributes related to affordance and cognitive load. A new attribute related to affordance of 3D scenes is proposed (termed dynamic affordance) and its role in impacting user comprehension in data-rich 3D training environments is studied. The framework is presented for the domain of orthopedic surgery. Rigorous user-involved assessment of the framework and simulation approach has highlighted the positive impact of the HCI-based framework and attributes on the acquisition of skills and knowledge by healthcare users

Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms

This study explores the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset using augmented images. The training results demonstrated that all models achieved high accuracy, indicating their ability to accurately learn and classify chicken eggs’ fertility state. However, when evaluated on the testing set, variations in accuracy and performance were observed. VGG16 achieved a high accuracy of 0.9803 on the testing set but had challenges in accurately detecting fertile eggs, as indicated by a NaN sensitivity value. ResNet50 also achieved an accuracy of 0.98 but struggled to identify fertile and non-fertile eggs, as suggested by NaN values for sensitivity and specificity. However, InceptionNet demonstrated excellent performance, with an accuracy of 0.9804, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.9615 for identifying non-fertile eggs. MobileNet achieved an accuracy of 0.9804 on the testing set; however, it faced challenges in accurately classifying the fertility status of chicken eggs, as indicated by NaN values for both sensitivity and specificity. While the models showed promise during training, variations in accuracy and performance were observed during testing. InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. Further optimization and fine-tuning of the models are necessary to address the limitations in accurately detecting fertile and non-fertile eggs. This study highlights the potential of CNN-Transfer Learning for nondestructive fertility detection and emphasizes the need for further research to enhance the models’ capabilities and ensure accurate classification